PROJECT SUMMARY The goal of this proposal is to create a method for characterizing glycosylation features based around flow cytometry employing glycan-specific reagents (lectins, antibodies, Lectenz) conjugated to spectrally-unique microspheres (beads) to detect glycan features. By combining the detection elements into a multiplexed array, an analysis can be obtained in minutes on a basic cytometer. The approach, which we call GlycoSense, can be thought of as a soluble 3D-analog of a 2D lectin microarray, and offers several unique and important advantages, including ease of use, statistical confidence, reproducibility, high sensitivity, and the benefits of equilibrium binding kinetics. In addition, this approach will enable G&D to leverage its proprietary experience in producing carbohydrate-sensing reagents. Because the method is rapid and cost-effective, it can be adopted by research laboratories without the need to rely exclusively on more sophisticated, and costly, MS/LC-MS based approaches that are typically only offered through core facilities or CROs. By adopting a familiar experimental platform (flow cytometry) there will be the additional advantage that the technology can be transferred readily into research groups with little or no expertise in the complex area of glycoprofiling. A GlycoSense analysis provides a quantified measure (a ?glycoprint?) of the overall glycosylation of a sample in terms of the composition and linkages for key glycofeatures. There are four specific aims in this proposal. First, optimal reagents and standards will be evaluated for their performance in our method. Second, the GlycoSense data will be confirmed by traditional methods. Third, a kit and SOP will be developed and cross-validated. Fourth, to facilitate adoption in a laboratory environment, we will create a software interface that converts raw flow cytometry data into a convenient graphical format to present the glycosylation analysis. Software development will also assist in data management, standardization of protocols, detection of systematic errors, and statistical analysis. We will work with the academic labs and biopharma companies that have expressed interest in our technology to beta-test our kits.